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> I wonder, is the dual-use of GPUs a significant engineering bottleneck for Nvidia et al? Not much. GPUs became array processing engines in the early 2000s an
by weebull 3y ago
> I wonder, is the dual-use of GPUs a significant engineering bottleneck for Nvidia et al?
Not much. GPUs became array processing engines in the early 2000s and the amount of residual fixed function graphics hardware is tiny now.
The biggest thing is that a lot of network evaluation is being done at lower precision (BF16/ FP8 / ternary) now, but my understanding is that training the networks still requires FP32. As these companies are really targeting the training market the high precision modes needed for graphics can't be thrown away. If a company was to target the end user, then you could optimise for lower precision, but end users find dedicated neural net acceleration a luxury they can get for free by buying a GPU.